-
Notifications
You must be signed in to change notification settings - Fork 1
/
step-4-thalamus-unreal.py
296 lines (228 loc) · 9.83 KB
/
step-4-thalamus-unreal.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
"""
Created on Sat Feb 3 11:12:30 2024
@author: celinesoeiro
"""
import numpy as np
import pandas as pd
import gc # Garbage Collector
from random import seed, random
seed(1)
random_factor = random()
from tcm_params import TCM_model_parameters, coupling_matrix_normal, coupling_matrix_PD
from model_plots import plot_heat_map, layer_raster_plot, plot_voltages, plot_raster_2
from TR_nucleus import TR_nucleus
from TC_nucleus import TC_nucleus
from S_nucleus import S_nucleus
from M_nucleus import M_nucleus
from D_nucleus import D_nucleus
from CI_nucleus import CI_nucleus
print("-- UNREAL")
# =============================================================================
# INITIAL VALUES
# =============================================================================
print("-- Initializing the global values")
neuron_quantities = TCM_model_parameters()['neuron_quantities']
neuron_per_structure = TCM_model_parameters()['neuron_per_structure']
neuron_params = TCM_model_parameters()['neuron_paramaters']
neuron_types_per_structure = TCM_model_parameters()['neuron_types_per_structure']
currents = TCM_model_parameters()['currents_per_structure']
p = TCM_model_parameters()['synapse_total_params']
noise = TCM_model_parameters()['noise']
# Neuron quantities
n_S = neuron_quantities['S']
n_M = neuron_quantities['M']
n_D = neuron_quantities['D']
n_CI = neuron_quantities['CI']
n_TR = neuron_quantities['TR']
n_TC = neuron_quantities['TC']
n_Hyper = neuron_quantities['HD']
n_total = neuron_quantities['total']
n_CI_FS = neuron_per_structure['neurons_ci_1']
n_CI_LTS = neuron_per_structure['neurons_ci_2']
n_D_RS = neuron_per_structure['neurons_d_1']
n_D_IB = neuron_per_structure['neurons_d_2']
n_S_RS = neuron_per_structure['neurons_s_1']
n_S_IB = neuron_per_structure['neurons_s_2']
vr = TCM_model_parameters()['vr']
dt = TCM_model_parameters()['dt']
sim_time = TCM_model_parameters()['simulation_time']
T = TCM_model_parameters()['simulation_time_ms']
sim_steps = TCM_model_parameters()['simulation_steps']
time_v = TCM_model_parameters()['time_vector']
time = np.arange(1, sim_steps)
# =============================================================================
# COUPLING MATRIXES
# =============================================================================
# Weight Matrix Normal Condition
Z_N = coupling_matrix_normal()['matrix']
# Weight Matrix Parkinsonian Desease Condition
Z_PD = coupling_matrix_PD()['matrix']
# normalizing Normal coupling matrix
Z_N_norm = Z_N/np.linalg.norm(Z_N)
# normalizing PD coupling matrix
Z_PD_norm = Z_PD/np.linalg.norm(Z_PD)
print("-- Printing the coupling matrixes")
CM_Normal = pd.DataFrame(Z_N_norm, columns=['S', 'M', 'D', 'CI', 'TC', 'TR'])
CM_PD = pd.DataFrame(Z_PD_norm, columns=['S', 'M', 'D', 'CI', 'TC', 'TR'])
plot_heat_map(matrix_normal = CM_Normal, matrix_PD = CM_PD)
# =============================================================================
# NEURON VARIABELS
# =============================================================================
print("-- Initializing the neuron variables")
## Post-Synaptic Currents
PSC_S = np.zeros((1, sim_steps))
PSC_M = np.zeros((1, sim_steps))
PSC_D = np.zeros((1, sim_steps))
PSC_CI = np.zeros((1, sim_steps))
PSC_TC = np.zeros((1, sim_steps))
PSC_TR = np.zeros((1, sim_steps))
## Thalamus-Cortex coupling params
PSC_T_D = np.zeros((1, sim_steps)) # from Thalamus to D
PSC_D_T = np.zeros((1, sim_steps)) # from D to Thalamus
## S
AP_S = np.zeros((n_S, sim_steps))
v_S = np.zeros((n_S, sim_steps))
u_S = np.zeros((n_S, sim_steps))
for i in range(n_S):
v_S[i][0] = vr
u_S[i][0] = neuron_params['b_S'][0][0]*vr
u_S_syn = np.zeros((1, p))
R_S_syn = np.ones((1, p))
I_S_syn = np.zeros((1, p))
del i
## M
AP_M = np.zeros((n_M, sim_steps))
v_M = np.zeros((n_M, sim_steps))
u_M = np.zeros((n_M, sim_steps))
for i in range(n_M):
v_M[i][0] = vr
u_M[i][0] = neuron_params['b_M'][0][0]*vr
u_M_syn = np.zeros((1, p))
R_M_syn = np.ones((1, p))
I_M_syn = np.zeros((1, p))
del i
## D
AP_D = np.zeros((n_D, sim_steps))
v_D = np.zeros((n_D, sim_steps))
u_D = np.zeros((n_D, sim_steps))
for i in range(n_D):
v_D[i][0] = vr
u_D[i][0] = neuron_params['b_D'][0][0]*vr
## D - Self
u_D_syn = np.zeros((1, p))
R_D_syn = np.ones((1, p))
I_D_syn = np.zeros((1, p))
del i
## CI
AP_CI = np.zeros((n_CI, sim_steps))
v_CI = np.zeros((n_CI, sim_steps))
u_CI = np.zeros((n_CI, sim_steps))
for i in range(n_CI):
v_CI[i][0] = vr
u_CI[i][0] = neuron_params['b_CI'][0][0]*vr
u_CI_syn = np.zeros((1, p))
R_CI_syn = np.ones((1, p))
I_CI_syn = np.zeros((1, p))
del i
## TC
AP_TC = np.zeros((n_TC, sim_steps))
v_TC = np.zeros((n_TC, sim_steps))
u_TC = np.zeros((n_TC, sim_steps))
for i in range(n_TC):
v_TC[i][0] = vr
u_TC[i][0] = neuron_params['b_TC'][0][0]*vr
u_TC_syn = np.zeros((1, p))
R_TC_syn = np.ones((1, p))
I_TC_syn = np.zeros((1, p))
del i
## TR
AP_TR = np.zeros((n_TR, sim_steps))
v_TR = np.zeros((n_TR, sim_steps))
u_TR = np.zeros((n_TR, sim_steps))
for i in range(n_TR):
v_TR[i][0] = vr
u_TR[i][0] = neuron_params['b_TR'][0][0]*vr
u_TR_syn = np.zeros((1, p))
R_TR_syn = np.ones((1, p))
I_TR_syn = np.zeros((1, p))
# =============================================================================
# MAIN
# =============================================================================
print("-- Running the model")
for t in time:
# =============================================================================
# TR
# =============================================================================
v_TR, u_TR, PSC_TR, u_TR_syn, I_TR_syn, R_TR_syn = TR_nucleus(t, v_TR, u_TR, AP_TR, PSC_TR, PSC_TC, PSC_CI, PSC_D_T, PSC_M, PSC_S, u_TR_syn, R_TR_syn, I_TR_syn)
# =============================================================================
# TC
# =============================================================================
v_TC, u_TC, PSC_TC, u_TC_syn, I_TC_syn, R_TC_syn, PSC_T_D = TC_nucleus(t, v_TC, u_TC, AP_TC, PSC_TC, PSC_S, PSC_M, PSC_D_T, PSC_TR, PSC_CI, PSC_T_D, R_TC_syn, u_TC_syn, I_TC_syn)
# =============================================================================
# S
# =============================================================================
v_S, u_S, PSC_S, u_S_syn, I_S_syn, R_S_syn = S_nucleus(t, v_S, u_S, AP_S, PSC_S, PSC_M, PSC_D, PSC_CI, PSC_TC, PSC_TR, u_S_syn, R_S_syn, I_S_syn)
# =============================================================================
# M
# =============================================================================
v_M, u_M, PSC_M, u_M_syn, I_M_syn, R_M_syn = M_nucleus(t, v_M, u_M, AP_M, PSC_M, PSC_S, PSC_D, PSC_CI, PSC_TC, PSC_TR, u_M_syn, R_M_syn, I_M_syn)
# =============================================================================
# D
# =============================================================================
v_D, u_D, PSC_D, u_D_syn, I_D_syn, R_D_syn, PSC_D_T = D_nucleus(t, v_D, u_D, AP_D, PSC_D, PSC_S, PSC_M, PSC_T_D, PSC_CI, PSC_TR, PSC_D_T, u_D_syn, R_D_syn, I_D_syn)
# =============================================================================
# CI
# =============================================================================
v_CI, u_CI, PSC_CI, u_CI_syn, I_CI_syn, R_CI_syn = CI_nucleus(t, v_CI, u_CI, AP_CI, PSC_CI, PSC_D, PSC_M, PSC_S, PSC_TC, PSC_TR, u_CI_syn, R_CI_syn, I_CI_syn)
# =============================================================================
# PLOTS
# =============================================================================
print("-- Plotting the results")
plot_voltages(n_neurons = n_S, voltage = v_S, title = "v - Layer S", neuron_types = neuron_types_per_structure['S'])
layer_raster_plot(n = n_S, AP = AP_S, sim_steps = sim_steps, layer_name = 'S', dt = dt)
print('APs in S layer = ', np.count_nonzero(AP_S))
plot_voltages(n_neurons = n_M, voltage = v_M, title = "v - Layer M", neuron_types = neuron_types_per_structure['M'])
layer_raster_plot(n = n_M, AP = AP_M, sim_steps = sim_steps, layer_name = 'M', dt = dt)
print('APs in M layer = ', np.count_nonzero(AP_M))
plot_voltages(n_neurons = n_D, voltage = v_D, title = "v - Layer D", neuron_types=neuron_types_per_structure['D'])
layer_raster_plot(n = n_D, AP = AP_D, sim_steps = sim_steps, layer_name = 'D', dt = dt)
print('APs in D layer = ', np.count_nonzero(AP_D))
plot_voltages(n_neurons = n_CI, voltage = v_CI, title = "Layer CI", neuron_types=neuron_types_per_structure['CI'])
layer_raster_plot(n = n_CI, AP = AP_CI, sim_steps = sim_steps, layer_name = 'CI', dt = dt)
print('APS in CI layer = ',np.count_nonzero(AP_CI))
plot_voltages(n_neurons = n_TC, voltage = v_TC, title = "TC", neuron_types=neuron_types_per_structure['TC'])
layer_raster_plot(n = n_TC, AP = AP_TC, sim_steps = sim_steps, layer_name = 'TC', dt = dt)
print('APS in TC layer = ',np.count_nonzero(AP_TC))
plot_voltages(n_neurons = n_TR, voltage = v_TR, title = "TR", neuron_types=neuron_types_per_structure['TR'])
layer_raster_plot(n = n_TR, AP = AP_TR, sim_steps = sim_steps, layer_name = 'TR', dt = dt)
print('APS in TR layer = ',np.count_nonzero(AP_TR))
plot_raster_2(
sim_steps=sim_steps,
sim_time=sim_time,
dt = dt,
chop_till = 0,
n_TR = n_TR,
n_TC = n_TC,
n_CI = n_CI,
n_D = n_D,
n_M = n_M,
n_S = n_S,
n_total = n_total,
n_CI_FS = n_CI_FS,
n_CI_LTS = n_CI_LTS,
n_D_RS = n_D_RS,
n_D_IB = n_D_IB,
n_S_RS = n_S_RS,
n_S_IB = n_S_IB,
spike_times_TR = AP_TR,
spike_times_TC = AP_TC,
spike_times_CI = AP_CI,
spike_times_D = AP_D,
spike_times_M = AP_M,
spike_times_S = AP_S
)
print("-- Done!")
b = neuron_params['b_CI']
b + np.random.rand(10,1)
0.2*np.random.rand(1,10)
-65 + 0.2*np.random.rand(1,10)**2